code: def suggest(self, n_suggestions: int) -> list[Suggestion]:
      suggestions: list[Suggestion] = list()
      for _ in range(n_suggestions):
          trial = self.study.ask()
          params = self._suggest(trial)
          sid = hash(frozenset(params.items()))
          self.current_trials[sid] = trial.number
          suggestions.append(params)
  
      return suggestions
  
file_path: optuna/benchmarks/bayesmark/optuna_optimizer.py
imports: from __future__ import annotations
  from typing import Any
  from typing import Dict
  from typing import Union
  import numpy
  from bayesmark.abstract_optimizer import AbstractOptimizer
  from bayesmark.experiment import experiment_main
  import optuna
  from optuna import pruners
  from optuna import samplers
  from optuna.integration.botorch import BoTorchSampler
  from optuna.integration.cma import PyCmaSampler
label_desc: Use image node_id 3 for calling the OptunaOptimizer obj's underlying member method code with example usage: obj.suggest(n_suggestions) and returns: suggestions